Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

Integrating IVHM and asset design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future

Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm).

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%

ARCHITECTURE FOR A CBM+ AND PHM CENTRIC DIGITAL TWIN FOR WARFARE SYSTEMS

The Department of the Navy’s continued progression from time-based maintenance into condition-based maintenance plus (CBM+) shows the importance of increasing operational availability (Ao) across fleet weapon systems. This capstone uses the concept of digital efficiency from a digital twin (DT) combined with a three-dimensional (3D) direct metal laser melting printer as the physical host on board a surface vessel. The DT provides an agnostic conduit for combining model-based systems engineering with a digital analysis for real-time prognostic health monitoring while improving predictive maintenance. With the DT at the forefront of prioritized research and development, the 3D printer combines the value of additive manufacturing with complex systems in dynamic shipboard environments. To demonstrate that the DT possesses parallel abilities for improving both the physical host’s Ao and end-goal mission, this capstone develops a DT architecture and a high-level model. The model focuses on specific printer components (deionized [DI] water level, DI water conductivity, air filters, and laser motor drive system) to demonstrate the DT’s inherent effectiveness towards CBM+. To embody the system of systems analysis for printer suitability and performance, more components should be evaluated and combined with the ship’s environment data. Additionally, this capstone recommends the use of DTs as a nexus into more complex weapon systems while using a deeper level of design of experiment.Outstanding ThesisCivilian, Department of the NavyCommander, United States NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques